The paper introduces a new concept – discovery power – that can be used to characterize an enterprise modeling language. The concept is different from, but connected to, the concept of expressive power. The concept is defined as “the degree of help provided by the structure of an enterprise modeling language to expand a partly built model or fill gaps in it”. The paper also suggests a way of evaluating the discovery power of enterprise modeling languages.
Model-Driven Development (MDD) is considered an effective technique for Internet of Things (IoT) application development. Our observation is that existing model-based approaches for IoT solutions focus on the software and systems perspective and show a need for more integration with organizational and business model aspects. Therefore, we developed a method and tool support for developing IoT applications in the field of air conditioning facilities. In this work, we applied quality criteria to evaluate the included Domain-Specific Modeling Language (DSML). To practically validate the modeling language as such and also the way it can be used and supported by the tool, we performed a real-world use case. The main contributions of this paper are a quality evaluation of the DSML and the tool support and lessons learned from both.
Traceability is a central concept in the domain of Enterprise Engineering. Indeed, the controlled transformation of the more business-oriented requirements layers to designs and runtime code has been studied over the years in domains such as IT Governance, Enterprise Architecture, and Software Development. However, traceability on the entire flow from business to IT is often lacking in complex projects in industry, thereby hampering the agility, productivity, and resilience of organizations in the digital era. Consequentially, organizations often fail to realize IT business value. Therefore, we investigate how the traceability issue in IT can be defined, and to what extent traceability issues still exist in practice based on 28 interviews with 16 interviewees from various organizations, differing in industry, size, and geographical location. Results indicate that opinions on the value and importance of traceability vary greatly, along with the views on how realistic it is to establish traceability in practice. Also, available theories and frameworks do not seem to be applied in many cases, even though they are not completely unknown to the interviewees. Clearly, there is a gap between theory and practice regarding traceability. The reasons for this are not entirely clear from this research. However, we call for new, holistic, and integrated research on traceability across multiple domains, highlighting the multidimensionality of the traceability issue in IT, and fostered in the Enterprise Engineering community.
In enterprise data management, the development of APIs for integrating diverse information systems often entails repetitive and labor-intensive tasks, such as translating variables and methods between systems. The advent of low-code platforms has significantly altered this landscape, facilitating the automatic and swift generation of APIs for both incoming and outgoing data and service actions. This paper explores a new approach using the Design and Engineering Methodology for Organizations (DEMO) data models within a low-code platform. Our methodology simplifies the API creation process by using DEMO’s Fact and Action models. Using a low-code platform, we enable users to efficiently generate endpoints for various functionalities, ranging from basic data item lists to complex query results, all achieved through intuitive drag-and-drop operations within a user-friendly graphical interface. This approach not only streamlines the development of APIs for internal tasks but also eases integration with external systems. Moreover, our approach includes the automated scanning of data from external APIs. Utilizing a user-friendly GUI, our system can automatically retrieve data from external sources and align it with internal data, ensuring consistent integration. This paper details this approach, emphasizing its effectiveness in integrating external information into local systems.
The Marispace-X project aims to create a digital ecosystem of providers and users of data from the oceans. For this purpose, different use cases (munitions detection, biological climate protection, the construction of offshore wind platforms and the testing of IoT sensors) are used as examples to model the benefits of the planned ecosystem. One of the main focuses of the project is to analyze and build emerging business models. Most research in this sector is focussed on singular business models, while industry specific business models are underrepresented. The problems in building such an ecosystem are not only technical, but also social in nature. Therefore, this work aims to illustrate how academia and industry can work together to address problems and develop possible solutions for Digital Business Ecosystems (DBE). This conjunction of problems, i.e., the view of an industry domain as a business model and the view of digital business ecosystems, motivate this research. This work shows how to start building an ecosystem or platform and how to sensitize relevant stakeholders to existing challenges in designing ecosystems. Furthermore, some solution approaches are presented to address sub-problems in business model design, especially in the area of value creation through data. Lessons learned and further open challenges can be found at the end of the paper. The contributions of this paper are: (1) an emerging digital business ecosystem with different roles, (2) a problem/goal model for science-communication with relevant stakeholders, (3) the application of an analysis tool for business models with a FAIR-Data Value Chain, (4) remaining challenges, especially in the areas of ecosystem governance and modelling.
